bioRxivDevelopmental BiologyPreprint
A stem cell knockout village reveals lineage rewiring and a non-canonical islet cell fate in monogenic diabetes
Dingyu LiuiD, Bicna SongiD, Zhaoheng LiiD, Stephen ZhangiD, Tabassum FabihaiD, Jiahui ZhaoiD, Ayaka InokiiD, Julie PiccandiD, Chew-Li Soh, Gary DixoniD, Aaron ZhongiD, Nan Hu, Renhe LuoiD, Batu Ozlusen, Vipin K. MenoniD, Ting Zhou, Xiaojie QiuiD, Gérard GradwohliD, Dapeng YangiD, Kushal K. DeyiD, Wei Sun, Wei Li, Danwei HuangfuiD
Abstract
Genetics studies have identified a core set of regulators essential for pancreatic {beta} cell development, many of which are mutated in monogenic diabetes. However, how these mutations alter developmental trajectories to produce pathological cell states remains elusive. Here we introduce a knockout village framework that enables longitudinal scRNA-seq profiling of 79 human pluripotent stem cell mutant lines targeting 30 developmental regulators, including 15 diabetes genes, across five islet differentiation stages. We show that loss of lineage regulators impairs {beta} cell formation in a stage-specific manner and rewires developmental trajectories towards competing lineages. Notably, several monogenic diabetes gene mutations drive a shift from {beta} cells to enterochromaffin (EC)-like cells, a recently recognized non-canonical islet cell fate. These EC-like cells exhibit incomplete activation of hormone regulation programs, along with elevated neuron signatures. Leveraging the diversity of cell fate outcomes across mutants, we predicted and experimentally validated ISL1 as a key downstream effector of PDX1 and PAX6 that safeguards {beta} cell identity against an EC-like fate. Together, our findings reveal cell fate rewiring as a widespread, previously underappreciated pathological mechanism in monogenic diabetes and establish a scalable platform for uncovering developmental vulnerabilities in human genetic disorders.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 3
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